
import numpy as np   
from sklearn.linear_model import LinearRegression
import pandas as pd    
import matplotlib.pyplot as plt 
%matplotlib inline 
import seaborn as sns
from sklearn.model_selection import train_test_split # Sklearn package's randomized data splitting function
import json
from sklearn.cluster import KMeans
from scipy.stats import zscore
from sklearn.preprocessing import StandardScaler
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn.decomposition import PCA
# logistic regression for feature importance
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot
import seaborn as sb
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score,confusion_matrix,precision_score,recall_score,classification_report
from scipy.stats import uniform
from collections import Counter
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from scipy.stats import randint as sp_randint
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV


dfsignal_mn = pd.read_csv("signal-data.csv") 
dfsignal_mn.shape

(1567, 592)


dfsignal_mn.head()


dfsignal_mn.describe()
dfsignal= dfsignal_mn.drop(["Time"], axis = 1)


percent_missing = dfsignal.isnull().sum() * 100 / len(dfsignal)
print(len(percent_missing))
missing_value_df = pd.DataFrame({'column_name': dfsignal.columns,
                                 'percent_missing': percent_missing})
for x in missing_value_df.column_name:        
        nrows=missing_value_df.percent_missing.loc[x]
        if nrows>20:
            dfsignal.drop(x, axis=1, inplace=True)
        elif (nrows>0) & (nrows<20):
            mean_value=dfsignal[x].mean()
            dfsignal[x].fillna(value=mean_value, inplace=True)

591


dfsignal


nunique = dfsignal.nunique()
cols_to_drop = nunique[nunique == 1].index
dfsignal.drop(cols_to_drop, axis=1,inplace=True)


nunique = dfsignal.nunique()
cols_to_drop = nunique[nunique <= 26].index
dfsignal.drop(cols_to_drop[:-1], axis=1,inplace=True)


corr = dfsignal.corr()

columns = np.full((corr.shape[0],), True, dtype=bool)
for i in range(corr.shape[0]):
    for j in range(i+1, corr.shape[0]):
        if corr.iloc[i,j] >= 0.9:
            if columns[j]:
                columns[j] = False
selected_columns = dfsignal.columns[columns]
data_selected = dfsignal[selected_columns]


data_selected.shape

(1567, 247)


selected_columns = selected_columns[:-1]
import statsmodels.api as sm
def backwardElimination(x, y, sl, columns):
    numVars = len(x[0])
    for i in range(0, numVars):
        regressor_OLS = sm.OLS(y,x).fit()
        maxVar = max(regressor_OLS.pvalues).astype(float)
        if maxVar > sl:
            for j in range(0, numVars - i):
                if (regressor_OLS.pvalues[j].astype(float) == maxVar):
                    x = np.delete(x, j, 1)
                    columns = np.delete(columns, j)
                    
    regressor_OLS.summary()
    return x,columns
SL = 0.05
data_modeled, selected_columns = backwardElimination(data_selected.iloc[:,:-1].values, data_selected.iloc[:,-1].values, SL, selected_columns)


result = pd.DataFrame()
result['Pass/Fail'] = data_selected.iloc[:,-1]


data = pd.DataFrame(data = data_modeled, columns = selected_columns)
data['Pass/Fail']=dfsignal['Pass/Fail']
data


from statsmodels.stats.outliers_influence import variance_inflation_factor
vdata=data.iloc[:,:-1]
# VIF dataframe
vif_data = pd.DataFrame()
vif_data["feature"] = vdata.columns

# calculating VIF for each feature
vif_data["VIF"] = [variance_inflation_factor(vdata.values, i)
                          for i in range(len(vdata.columns))]
print(vif_data)

   feature           VIF
0       14     11.940702
1       25    175.238074
2       26    370.540041
3       32   4277.068869
4       34  28530.171887
5       36  23937.778768
6       43  14968.616875
7       48   2921.908537
8       53  20422.087557
9       56  85523.680820
10      59      2.766615
11      63     24.384985
12      64    174.105387
13      65    115.024502
14      67      6.543354
15      68   3129.395142
16      75      1.281316
17      99      1.056646
18     129      1.526284
19     130    117.767869
20     132   2852.928821
21     145      7.100128
22     155      1.270957
23     166      7.571273
24     177      3.266166
25     195      1.659703
26     197     30.620512
27     202     14.838115
28     203     35.090705
29     210      6.954719
30     211      6.506080
31     214      3.813443
32     216     13.165876
33     473     10.133082
34     488      3.363852
35     510      3.762699
36     511      1.756403
37     548    709.338569
38     562   1619.714766
39     563     53.556936
40     565      6.494352


vdata.drop(vif_data['feature'][vif_data['VIF']>=10],axis=1, inplace=True)


vdata


vif_data_new = pd.DataFrame()

vif_data_new["feature"] = vdata.columns
  
# calculating VIF for each feature
vif_data_new["VIF"] = [variance_inflation_factor(vdata.values, i)
                          for i in range(len(vdata.columns))]
print(vif_data_new)

   feature       VIF
0       59  1.694078
1       67  1.362284
2       75  1.217979
3       99  1.024910
4      129  1.292777
5      145  5.873489
6      155  1.253031
7      166  5.951414
8      177  3.001032
9      195  1.506853
10     210  6.263840
11     211  5.567122
12     214  3.680005
13     488  2.807252
14     510  3.269180
15     511  1.692072
16     565  4.506259


vdata['Pass/Fail']=dfsignal['Pass/Fail']
data=vdata


data['59'].value_counts().head(10).plot.bar()

<AxesSubplot:>


data['75'].value_counts().head(10).plot.bar()

<AxesSubplot:>


data['99'].value_counts().head(10).plot.bar()

<AxesSubplot:>


data['129'].value_counts().head(10).plot.bar()

<AxesSubplot:>


sns.FacetGrid(data,hue='Pass/Fail',height=4).map(sns.histplot,"155").add_legend()

<seaborn.axisgrid.FacetGrid at 0x2a7bbc3cdf0>


sns.FacetGrid(data,hue='Pass/Fail',height=4).map(sns.histplot,"177").add_legend()

<seaborn.axisgrid.FacetGrid at 0x2a7bc156d60>


sns.set()
sns.pairplot(data, height = 3,hue="Pass/Fail")
plt.show();


data["Pass/Fail"].replace(-1,0,inplace=True)


data


Prd=data.iloc[:,0:-1]
tr=data.iloc[:,-1]


sns.countplot(x=tr,data=data)

<AxesSubplot:xlabel='Pass/Fail', ylabel='count'>


X_train,X_test, Y_train, Y_test = train_test_split(Prd, tr, test_size=0.20, random_state=2)


from scipy.stats import f_oneway
data1 =Prd
data2 = X_train
data3 = X_test
stat, p = f_oneway(data1, data2, data3)
print('P value:',p)
if p.all() > 0.05:
    print('The train data and test data is same distribution of original data')
else:
    print('The train data and data test is different distributions of original data')

P value: [0.83350701 0.88948718 0.33192932 0.57916996 0.29497177 0.5124327
 0.98669001 0.08696235 0.88753047 0.73754534 0.39086373 0.67589483
 0.93649239 0.05699728 0.91314248 0.53251251 0.69272075]
The train data and test data is same distribution of original data


from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
x_train_scale = scaler.fit_transform(X_train)
x_test_scale = scaler.fit_transform(X_test)


knn = KNeighborsClassifier()
knn.fit(x_train_scale, Y_train)

KNeighborsClassifier()


Xtrain_predict = knn.predict(x_train_scale)
Xtest_predict = knn.predict(x_test_scale)


print("Model Accuracy on Train data: {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict)))
print("Model Accuracy on test data: {0:.2f}".format(accuracy_score(Y_test, Xtest_predict)))

Model Accuracy on Train data: 0.93
Model Accuracy on test data: 0.94


param_grid = {'n_neighbors': list(range(1,9)),
             'algorithm': ('auto', 'ball_tree', 'kd_tree' , 'brute') }


#GridSearchCV tuning parameter
gs_param = GridSearchCV(knn,param_grid,cv=2)
gs_param.fit(x_train_scale, Y_train)

GridSearchCV(cv=2, estimator=KNeighborsClassifier(),
             param_grid={'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute'),
                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8]})


print(gs_param.best_params_)

{'algorithm': 'auto', 'n_neighbors': 6}


Xtrain_predict_gs = gs_param.predict(x_train_scale)
Xtest_predict_gs = gs_param.predict(x_test_scale)


print("GridSearchCV Model Accuracy on Train data After tuning : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_gs)))
print("GridSearchCV Model Accuracy on test data After tuning : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_gs)))

GridSearchCV Model Accuracy on Train data After tuning : 0.93
GridSearchCV Model Accuracy on test data After tuning : 0.94


#RandomizedSearchCV tuning parameter
k_range=list(range(1,9))
weight_options = ['uniform', 'distance']
param_dist = dict(n_neighbors=k_range, weights=weight_options)


randomCV_param = RandomizedSearchCV(knn,param_distributions=param_dist, n_iter=12, scoring='accuracy', n_jobs=-1, random_state=1)


randomCV_param.fit(x_train_scale, Y_train)

RandomizedSearchCV(estimator=KNeighborsClassifier(), n_iter=12, n_jobs=-1,
                   param_distributions={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8],
                                        'weights': ['uniform', 'distance']},
                   random_state=1, scoring='accuracy')


Xtrain_predict_rand = randomCV_param.predict(x_train_scale)
Xtest_predict_rand = randomCV_param.predict(x_test_scale)


print("RandomizedSearchCV Model Accuracy on Train data After tuning : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_rand)))
print("RandomizedSearchCV Model Accuracy on test data After tuning : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_rand)))

RandomizedSearchCV Model Accuracy on Train data After tuning : 1.00
RandomizedSearchCV Model Accuracy on test data After tuning : 0.94


from sklearn import preprocessing

# normalize the data attributes
normalized_train = preprocessing.normalize(X_train)
normalized_test= preprocessing.normalize(X_test)


knn = KNeighborsClassifier()
knn.fit(normalized_train, Y_train)

KNeighborsClassifier()


Xtrain_predict1 = knn.predict(normalized_train)
Xtest_predict1 = knn.predict(normalized_test)


print("Model Accuracy on Train data: {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict1)))
print("Model Accuracy on test data: {0:.2f}".format(accuracy_score(Y_test, Xtest_predict1)))

Model Accuracy on Train data: 0.93
Model Accuracy on test data: 0.94


# evaluate accuracy
print("The final model accuracy score{0:.2f}".format(accuracy_score(Y_test, Xtest_predict1)))
print("The final model recall score {0:.2f}".format(recall_score(Y_test, Xtest_predict1)))
print("The final model precision score {0:.2f}".format(precision_score(Y_test, Xtest_predict1)))
print();
print(classification_report(Y_test,Xtest_predict1,labels=[1, 0]))

The final model accuracy score0.94
The final model recall score 0.00
The final model precision score 0.00

              precision    recall  f1-score   support

           1       0.00      0.00      0.00        20
           0       0.94      1.00      0.97       294

    accuracy                           0.94       314
   macro avg       0.47      0.50      0.48       314
weighted avg       0.88      0.94      0.91       314

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))


data.describe()


data.drop(['99','511'],axis=1, inplace=True)#dropping of column 99,511 because of 50% value contains 0.0000


Prd2=data.iloc[:,0:-1]
tr2=data.iloc[:,-1]


X_train2,X_test2, Y_train2, Y_test2 = train_test_split(Prd2, tr2, test_size=0.20, random_state=2)


from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
x_train_scale2 = scaler.fit_transform(X_train2)
x_test_scale2 = scaler.fit_transform(X_test2)


svc_model = SVC(C= .1, kernel='linear', gamma= 1)
svc_model.fit(X_train2, Y_train2)

SVC(C=0.1, gamma=1, kernel='linear')


svc_pred_train = svc_model.predict(x_train_scale2)
svc_pred_test = svc_model.predict(x_test_scale2)


print("Model Accuracy on Train data: {0:.2f}".format(accuracy_score(Y_train2, svc_pred_train)))
print("Model Accuracy on test data: {0:.2f}".format(accuracy_score(Y_test2, svc_pred_test)))

Model Accuracy on Train data: 0.93
Model Accuracy on test data: 0.94


from scipy.stats import reciprocal, uniform
param_dist2 = {"gamma": reciprocal(0.001, 0.1), "C": uniform(1, 10)}


randomCV_param2= RandomizedSearchCV(svc_model,param_distributions=param_dist2, n_iter=12, scoring='accuracy', n_jobs=-1, random_state=1)


randomCV_param2.fit(x_train_scale2, Y_train)

RandomizedSearchCV(estimator=SVC(C=0.1, gamma=1, kernel='linear'), n_iter=12,
                   n_jobs=-1,
                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A7C5672E80>,
                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A7CBB379A0>},
                   random_state=1, scoring='accuracy')


Xtrain_predict_rand2 = randomCV_param2.predict(x_train_scale2)
Xtest_predict_rand2 = randomCV_param2.predict(x_test_scale2)


print("RandomizedSearchCV SVM Model Accuracy on Train data After tuning : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_rand2)))
print("RandomizedSearchCV SVM Model Accuracy on test data After tuning : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_rand2)))

RandomizedSearchCV SVM Model Accuracy on Train data After tuning : 0.93
RandomizedSearchCV SVM Model Accuracy on test data After tuning : 0.94


clf = RandomForestClassifier(n_estimators=50)


param_dist_rfc = {"max_depth": [3, None],
              "max_features": sp_randint(1, 11),
              "min_samples_split": sp_randint(2, 11),
              "min_samples_leaf": sp_randint(1, 11),
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}


samples = 10  # number of random samples 
randomCV_clf = RandomizedSearchCV(clf, param_distributions=param_dist_rfc, n_iter=samples) #default cv = 3


randomCV_clf.fit(x_train_scale2, Y_train)

RandomizedSearchCV(estimator=RandomForestClassifier(n_estimators=50),
                   param_distributions={'bootstrap': [True, False],
                                        'criterion': ['gini', 'entropy'],
                                        'max_depth': [3, None],
                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A7C3DEB640>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A7C3DEB310>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A7C3DEBE50>})


Xtrain_predict_clf = randomCV_clf.predict(x_train_scale2)
Xtest_predict_clf = randomCV_clf.predict(x_test_scale2)


print("RandomizedSearchCV RandomForestClassifier Model Accuracy on Train data After tuning : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_clf)))
print("RandomizedSearchCV RandomForestClassifier Model Accuracy on test data After tuning : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_clf)))

RandomizedSearchCV RandomForestClassifier Model Accuracy on Train data After tuning : 0.93
RandomizedSearchCV RandomForestClassifier Model Accuracy on test data After tuning : 0.94


param_grid_clf = {"max_depth": [3, None],
              "max_features": [1, 3, 10],
              "min_samples_split": [2, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}


grid_search = GridSearchCV(clf, param_grid=param_grid_clf)
grid_search.fit(x_train_scale2, Y_train)

GridSearchCV(estimator=RandomForestClassifier(n_estimators=50),
             param_grid={'bootstrap': [True, False],
                         'criterion': ['gini', 'entropy'],
                         'max_depth': [3, None], 'max_features': [1, 3, 10],
                         'min_samples_leaf': [1, 3, 10],
                         'min_samples_split': [2, 3, 10]})


Xtrain_predict_clf_grid = grid_search.predict(x_train_scale2)
Xtest_predict_clf_grid = grid_search.predict(x_test_scale2)


print("GridSearchCV RandomForestClassifier Model Accuracy on Train data After tuning : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_clf_grid)))
print("GridSearchCV RandomForestClassifier Model Accuracy on test data After tuning : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_clf_grid)))

GridSearchCV RandomForestClassifier Model Accuracy on Train data After tuning : 1.00
GridSearchCV RandomForestClassifier Model Accuracy on test data After tuning : 0.94


dTree = DecisionTreeClassifier(criterion = 'gini',max_depth = 2, random_state=1)
dTree.fit(x_train_scale2, Y_train)

DecisionTreeClassifier(max_depth=2, random_state=1)


Xtrain_predict_dtr = dTree.predict(x_train_scale2)
Xtest_predict_dtr = dTree.predict(x_test_scale2)


print("Dtree Model Accuracy on Train data After tuning : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_dtr)))
print("Dtree Model Accuracy on test data After tuning : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_dtr)))

Dtree Model Accuracy on Train data After tuning : 0.94
Dtree Model Accuracy on test data After tuning : 0.94


print("Before OverSampling, counts of label '1': {}".format(sum(Y_train == 1)))
print("Before OverSampling, counts of label '0': {} \n".format(sum(Y_train == 0)))
  
# import SMOTE module from imblearn library
# pip install imblearn (if you don't have imblearn in your system)
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state = 2)
X_train_res, y_train_res = sm.fit_resample(X_train, Y_train.ravel())
  
print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))
print('After OverSampling, the shape of train_y: {} \n'.format(y_train_res.shape))
  
print("After OverSampling, counts of label '1': {}".format(sum(y_train_res == 1)))
print("After OverSampling, counts of label '0': {}".format(sum(y_train_res == 0)))

Before OverSampling, counts of label '1': 84
Before OverSampling, counts of label '0': 1169 

After OverSampling, the shape of train_X: (2338, 17)
After OverSampling, the shape of train_y: (2338,) 

After OverSampling, counts of label '1': 1169
After OverSampling, counts of label '0': 1169


dTree2 = DecisionTreeClassifier(criterion = 'gini',max_depth = 2, random_state=1)
dTree2.fit(X_train_res, y_train_res)

DecisionTreeClassifier(max_depth=2, random_state=1)


Xtrain_predict_dtr2 = dTree2.predict(X_train)
Xtest_predict_dtr2 = dTree2.predict(X_test)


print("Dtree Model Accuracy on Train data After over_sampling : {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_dtr2)))
print("Dtree Model Accuracy on test data After over_sampling : {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_dtr2)))

Dtree Model Accuracy on Train data After over_sampling : 0.81
Dtree Model Accuracy on test data After over_sampling : 0.81


from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
x_train_scale = scaler.fit_transform(X_train)
x_test_scale = scaler.fit_transform(X_test)


knn_f = KNeighborsClassifier()
knn_f.fit(x_train_scale, Y_train)

KNeighborsClassifier()


Xtrain_predict_f = knn_f.predict(x_train_scale)
Xtest_predict_f = knn_f.predict(x_test_scale)


print("Model Accuracy on Train data: {0:.2f}".format(accuracy_score(Y_train, Xtrain_predict_f)))
print("Model Accuracy on test data: {0:.2f}".format(accuracy_score(Y_test, Xtest_predict_f)))

Model Accuracy on Train data: 0.93
Model Accuracy on test data: 0.94

	Time	0	1	2	3	4	5	6	7	8	...	581	582	583	584	585	586	587	588	589	Pass/Fail
0	2008-07-19 11:55:00	3030.93	2564.00	2187.7333	1411.1265	1.3602	100.0	97.6133	0.1242	1.5005	...	NaN	0.5005	0.0118	0.0035	2.3630	NaN	NaN	NaN	NaN	-1
1	2008-07-19 12:32:00	3095.78	2465.14	2230.4222	1463.6606	0.8294	100.0	102.3433	0.1247	1.4966	...	208.2045	0.5019	0.0223	0.0055	4.4447	0.0096	0.0201	0.0060	208.2045	-1
2	2008-07-19 13:17:00	2932.61	2559.94	2186.4111	1698.0172	1.5102	100.0	95.4878	0.1241	1.4436	...	82.8602	0.4958	0.0157	0.0039	3.1745	0.0584	0.0484	0.0148	82.8602	1
3	2008-07-19 14:43:00	2988.72	2479.90	2199.0333	909.7926	1.3204	100.0	104.2367	0.1217	1.4882	...	73.8432	0.4990	0.0103	0.0025	2.0544	0.0202	0.0149	0.0044	73.8432	-1
4	2008-07-19 15:22:00	3032.24	2502.87	2233.3667	1326.5200	1.5334	100.0	100.3967	0.1235	1.5031	...	NaN	0.4800	0.4766	0.1045	99.3032	0.0202	0.0149	0.0044	73.8432	-1

	0	1	2	3	4	5	6	7	8	9	...	577	582	583	584	585	586	587	588	589	Pass/Fail
0	3030.93	2564.00	2187.7333	1411.1265	1.3602	100.0	97.6133	0.1242	1.500500	0.016200	...	14.9509	0.5005	0.0118	0.0035	2.3630	0.021458	0.016475	0.005283	99.670066	-1
1	3095.78	2465.14	2230.4222	1463.6606	0.8294	100.0	102.3433	0.1247	1.496600	-0.000500	...	10.9003	0.5019	0.0223	0.0055	4.4447	0.009600	0.020100	0.006000	208.204500	-1
2	2932.61	2559.94	2186.4111	1698.0172	1.5102	100.0	95.4878	0.1241	1.443600	0.004100	...	9.2721	0.4958	0.0157	0.0039	3.1745	0.058400	0.048400	0.014800	82.860200	1
3	2988.72	2479.90	2199.0333	909.7926	1.3204	100.0	104.2367	0.1217	1.488200	-0.012400	...	8.5831	0.4990	0.0103	0.0025	2.0544	0.020200	0.014900	0.004400	73.843200	-1
4	3032.24	2502.87	2233.3667	1326.5200	1.5334	100.0	100.3967	0.1235	1.503100	-0.003100	...	10.9698	0.4800	0.4766	0.1045	99.3032	0.020200	0.014900	0.004400	73.843200	-1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1562	2899.41	2464.36	2179.7333	3085.3781	1.4843	100.0	82.2467	0.1248	1.342400	-0.004500	...	11.7256	0.4988	0.0143	0.0039	2.8669	0.006800	0.013800	0.004700	203.172000	-1
1563	3052.31	2522.55	2198.5667	1124.6595	0.8763	100.0	98.4689	0.1205	1.433300	-0.006100	...	17.8379	0.4975	0.0131	0.0036	2.6238	0.006800	0.013800	0.004700	203.172000	-1
1564	2978.81	2379.78	2206.3000	1110.4967	0.8236	100.0	99.4122	0.1208	1.462862	-0.000841	...	17.7267	0.4987	0.0153	0.0041	3.0590	0.019700	0.008600	0.002500	43.523100	-1
1565	2894.92	2532.01	2177.0333	1183.7287	1.5726	100.0	98.7978	0.1213	1.462200	-0.007200	...	19.2104	0.5004	0.0178	0.0038	3.5662	0.026200	0.024500	0.007500	93.494100	-1
1566	2944.92	2450.76	2195.4444	2914.1792	1.5978	100.0	85.1011	0.1235	1.462862	-0.000841	...	22.9183	0.4987	0.0181	0.0040	3.6275	0.011700	0.016200	0.004500	137.784400	-1

	14	25	26	32	34	36	43	48	53	56	...	216	473	488	510	511	548	562	563	565	Pass/Fail
0	7.955800	0.8955	1.7730	83.3971	50.6170	49.3830	352.7173	141.2282	4.592	0.9317	...	0.030100	24.7625	53.109800	64.6707	0.0000	75.752	262.729683	0.679641	0.14561	-1
1	10.154800	1.2973	2.0143	84.9052	50.6596	49.3404	352.2445	145.8445	4.590	0.9324	...	0.073400	19.4865	194.437100	141.4365	0.0000	74.640	262.729683	0.679641	0.14561	-1
2	9.515700	1.3122	2.0295	84.7569	50.1530	49.8470	364.3782	141.0845	4.486	0.9139	...	0.034900	31.4674	191.758200	240.7767	244.2748	74.654	267.064000	0.903200	0.62190	1
3	9.605200	1.3137	2.0038	84.9105	50.5100	49.4900	363.0273	142.5427	4.486	0.9139	...	0.064300	37.8979	0.000000	113.5593	0.0000	72.442	268.228000	0.651100	0.16300	-1
4	10.566100	1.2887	1.9912	86.3269	50.2480	49.7520	353.3400	138.0882	4.624	0.9298	...	0.092400	17.0927	748.178100	148.0663	0.0000	79.156	262.729683	0.679641	0.14561	-1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1562	11.769200	1.2817	1.9540	83.8405	50.2482	49.7518	369.9009	138.0482	4.658	0.9204	...	0.083484	44.6402	352.616477	53.1915	235.7895	77.166	264.272000	0.567100	0.08770	-1
1563	9.162000	1.0870	1.8023	84.0623	50.2067	49.7934	351.4055	132.9045	4.585	0.9255	...	0.083484	19.9953	352.616477	29.4372	700.0000	73.254	266.832000	0.625400	0.13080	-1
1564	9.005371	1.2930	1.9435	85.8638	50.9333	49.0667	350.3145	142.8818	4.596	0.9353	...	0.083484	43.2736	352.616477	54.8330	0.0000	74.156	256.730000	0.820900	0.23880	-1
1565	9.735400	1.2875	1.9880	84.5602	50.6547	49.3453	370.5845	141.9164	4.584	0.9207	...	0.083484	47.1291	352.616477	78.4993	456.4103	73.254	264.272000	0.567100	0.08770	-1
1566	9.005371	1.3020	2.0085	83.3424	50.1940	49.8060	362.8100	134.0300	4.594	0.9187	...	0.083484	67.9605	352.616477	75.8621	317.6471	73.254	257.974000	0.619300	0.13070	-1

	59	67	75	99	129	145	155	166	177	195	210	211	214	488	510	511	565
0	-1.7264	0.9226	0.012600	-0.0251	-0.047300	0.085000	0.41	2.0	0.160	0.292	0.077200	0.059900	0.070400	53.109800	64.6707	0.0000	0.14561
1	0.8073	1.1598	-0.003900	-0.0158	-0.094600	0.060000	0.35	2.2	0.325	0.222	0.056600	0.048800	0.046800	194.437100	141.4365	0.0000	0.14561
2	23.8245	0.8694	-0.007800	0.1218	-0.189200	0.048300	0.47	2.1	0.326	0.139	0.033900	0.049400	0.040100	191.758200	240.7767	244.2748	0.62190
3	24.3791	0.9761	-0.055500	-0.0195	0.283800	0.039300	0.35	1.7	0.390	0.139	0.124800	0.046300	0.070800	0.000000	113.5593	0.0000	0.16300
4	-12.2945	0.9256	-0.053400	-0.0167	-0.567700	0.045100	0.43	3.9	0.922	0.250	0.091500	0.050600	0.079700	748.178100	148.0663	0.0000	0.14561
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1562	2.8182	0.9923	-0.006903	0.0884	0.000000	0.032200	0.23	5.9	0.159	0.088	0.088866	0.056755	0.083268	352.616477	53.1915	235.7895	0.08770
1563	-3.3555	0.4904	-0.006903	-0.0372	-0.141900	0.046800	0.28	2.7	0.414	0.249	0.088866	0.056755	0.083268	352.616477	29.4372	700.0000	0.13080
1564	1.1664	0.9078	-0.006903	-0.0177	-0.554228	0.063621	0.29	3.2	0.790	0.205	0.088866	0.056755	0.083268	352.616477	54.8330	0.0000	0.23880
1565	4.4682	0.9981	-0.006903	0.1451	-0.993400	0.034100	0.24	2.2	0.271	0.523	0.088866	0.056755	0.083268	352.616477	78.4993	456.4103	0.08770
1566	1.8718	0.9683	-0.006903	-0.0632	-0.554228	0.063621	0.23	3.9	0.465	0.181	0.088866	0.056755	0.083268	352.616477	75.8621	317.6471	0.13070

	59	67	75	99	129	145	155	166	177	195	210	211	214	488	510	511	565	Pass/Fail
0	-1.7264	0.9226	0.012600	-0.0251	-0.047300	0.085000	0.41	2.0	0.160	0.292	0.077200	0.059900	0.070400	53.109800	64.6707	0.0000	0.14561	0
1	0.8073	1.1598	-0.003900	-0.0158	-0.094600	0.060000	0.35	2.2	0.325	0.222	0.056600	0.048800	0.046800	194.437100	141.4365	0.0000	0.14561	0
2	23.8245	0.8694	-0.007800	0.1218	-0.189200	0.048300	0.47	2.1	0.326	0.139	0.033900	0.049400	0.040100	191.758200	240.7767	244.2748	0.62190	1
3	24.3791	0.9761	-0.055500	-0.0195	0.283800	0.039300	0.35	1.7	0.390	0.139	0.124800	0.046300	0.070800	0.000000	113.5593	0.0000	0.16300	0
4	-12.2945	0.9256	-0.053400	-0.0167	-0.567700	0.045100	0.43	3.9	0.922	0.250	0.091500	0.050600	0.079700	748.178100	148.0663	0.0000	0.14561	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1562	2.8182	0.9923	-0.006903	0.0884	0.000000	0.032200	0.23	5.9	0.159	0.088	0.088866	0.056755	0.083268	352.616477	53.1915	235.7895	0.08770	0
1563	-3.3555	0.4904	-0.006903	-0.0372	-0.141900	0.046800	0.28	2.7	0.414	0.249	0.088866	0.056755	0.083268	352.616477	29.4372	700.0000	0.13080	0
1564	1.1664	0.9078	-0.006903	-0.0177	-0.554228	0.063621	0.29	3.2	0.790	0.205	0.088866	0.056755	0.083268	352.616477	54.8330	0.0000	0.23880	0
1565	4.4682	0.9981	-0.006903	0.1451	-0.993400	0.034100	0.24	2.2	0.271	0.523	0.088866	0.056755	0.083268	352.616477	78.4993	456.4103	0.08770	0
1566	1.8718	0.9683	-0.006903	-0.0632	-0.554228	0.063621	0.23	3.9	0.465	0.181	0.088866	0.056755	0.083268	352.616477	75.8621	317.6471	0.13070	0

	59	67	75	99	129	145	155	166	177	195	210	211	214	488	510	511	565	Pass/Fail
count	1567.000000	1567.000000	1567.000000	1567.00000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000	1567.000000
mean	2.960241	16.715444	-0.006903	0.00154	-0.554228	0.063621	0.507171	2.788882	0.394760	0.287084	0.088866	0.056755	0.083268	352.616477	55.763508	275.979457	0.145610	0.066369
std	9.510891	306.912643	0.022121	0.06262	1.216967	0.026524	1.118837	1.119041	0.282813	0.394682	0.041741	0.024813	0.056021	250.104924	37.667659	329.454099	0.073713	0.249005
min	-28.988200	0.413700	-0.104900	-0.52830	-3.779000	0.021400	0.140000	0.800000	0.047000	0.080000	0.031900	0.002200	0.019300	0.000000	0.000000	0.000000	0.022400	0.000000
25%	-1.855450	0.891500	-0.019200	-0.02980	-0.898800	0.048050	0.240000	2.100000	0.222000	0.219500	0.065900	0.043950	0.057050	145.156850	35.324400	0.000000	0.087700	0.000000
50%	0.973600	0.978600	-0.006600	0.00000	-0.141900	0.058600	0.320000	2.600000	0.299000	0.259000	0.080300	0.053600	0.075900	352.511400	47.058800	0.000000	0.142800	0.000000
75%	4.337700	1.066050	0.006600	0.02980	0.047300	0.071800	0.460000	3.200000	0.423000	0.296000	0.099050	0.064100	0.093250	507.497050	64.228450	554.010700	0.166850	0.000000
max	168.145500	7272.828300	0.231500	0.88540	2.458000	0.250700	12.710000	21.100000	1.858000	4.838000	0.516400	0.322700	0.761500	997.518600	451.485100	1000.000000	0.689200	1.000000

Finalizing model¶